Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
International Journal of Learning Technology
Comparing State-of-the-Art Collaborative Filtering Systems
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Collaborative filtering using orthogonal nonnegative matrix tri-factorization
Information Processing and Management: an International Journal
Quantitative Analysis of Learning Object Repositories
IEEE Transactions on Learning Technologies
INCOS '09 Proceedings of the 2009 International Conference on Intelligent Networking and Collaborative Systems
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Beyond accuracy: evaluating recommender systems by coverage and serendipity
Proceedings of the fourth ACM conference on Recommender systems
Statistical profiles of highly-rated learning objects
Computers & Education
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Dataset-driven research for improving recommender systems for learning
Proceedings of the 1st International Conference on Learning Analytics and Knowledge
Predicting correctness of problem solving in ITS with a temporal collaborative filtering approach
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
A scalable privacy-preserving recommendation scheme via bisecting k-means clustering
Information Processing and Management: an International Journal
Hybrid recommendation approaches for multi-criteria collaborative filtering
Expert Systems with Applications: An International Journal
Using hybrid semantic information filtering approach in communities of practice of E-learning
Journal of Web Engineering
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Collaborative filtering (CF) algorithms are techniques used by recommender systems to predict the utility of items for users based on the similarity among their preferences and the preferences of other users. The enormous growth of learning objects on the internet and the availability of preferences of usage by the community of users in the existing learning object repositories (LORs) have opened the possibility of testing the efficiency of CF algorithms on recommending learning materials to the users of these communities. In this paper we evaluated recommendations of learning resources generated by different well known memory-based CF algorithms using two databases (with implicit and explicit ratings) gathered from the popular MERLOT repository. We have also contrasted the results of the generated recommendations with several existing endorsement mechanisms of the repository to explore possible relations among them. Finally, the recommendations generated by the different algorithms were compared in order to evaluate whether or not they were overlapping. The results found here can be used as a starting point for future studies that account for the specific context of learning object repositories and the different aspects of preference in learning resource selection.